5.2 Impact of the degree of randomness parameter in the Gauss-Markov mobility model

The above observations justify the usage of the average of the values of a performance metric for different values of [alpha] as a measure of the performance under the Gauss-Markov mobility model in figures 7 through 12 that compare the performance under the different mobility models.

In the case of the Gauss-Markov mobility model, the direction of movement of the nodes is restricted close to the initially assigned mean direction of movement.

Similarly, due to the temporal dependency associated with the Gauss-Markov mobility model, one cannot always find minimum hop paths lying on a straight line connecting the source and destination nodes.

For a given node velocity, the average hop count per minimum hop path under the City Section mobility model, Gauss-Markov mobility model and the Manhattan mobility model is respectively about 14%, 17% and 19% more than that incurred for the Random Waypoint mobility model in low-density networks.

The relatively poor lifetime of minimum hop routes determined under the Gauss-Markov mobility model can be attributed to the temporal dependency of the nodes in choosing their direction of movement.

The lifetime ratio for the Gauss-Markov mobility model is the lowest of all the four mobility models in low-density networks.

As for the Gauss-Markov mobility model, the greatest increase occurs when the network conditions have 200 stations.

As for the lowest average throughput value in both schemes is owned by Gauss-Markov mobility model, with a value of 0.467256 Mbps.

While the lowest PDR value is owned by Gauss-Markov mobility model by 91%, with average decrease of 4.21% from the best condition.

As for the lowest average PDR value in both schemes are owned by the Gauss-Markov mobility model, with an average PDR value is about 95.08%.

As for the average value of the largest energy consumption in both schemes is owned by Gauss-Markov mobility model, with a value of 5.77026 Joule.